Computer Science > Machine Learning

Abstract: We propose a novel and flexible approach to meta-learning for
learning-to-learn from only a few examples. Our framework is motivated by
actor-critic reinforcement learning, but can be applied to both reinforcement
and supervised learning. The key idea is to learn a meta-critic: an
action-value function neural network that learns to criticise any actor trying
to solve any specified task. For supervised learning, this corresponds to the
novel idea of a trainable task-parametrised loss generator. This meta-critic
approach provides a route to knowledge transfer that can flexibly deal with
few-shot and semi-supervised conditions for both reinforcement and supervised
learning. Promising results are shown on both reinforcement and supervised
learning problems.